Abstract
To promote the forecasting performance of traditional neural networks for non-stationary wind speed signal, an optimization algorithm was proposed based on wavelet analysis method and neural networks method. This optimization algorithm employed wavelet analysis method to make signal decomposition and reconstruction calculations for original wind speed series attain more steady sub-series. Then BP neural networks method was used to build unsteady prediction models for each layer to realize multi-step rolling forecast calculation. Simulation results show that the optimization algorithm can attain high-precision multi-step ahead forecast results, respectively improve forecast precision of traditional BP neural networks method by 55.56%, 32.43% and 34.58%, and the mean relative error of one-step, three-step and five-step ahead forecast are 0.48%, 1.50% and 2.97%. The optimization has signal decomposition and self-learning ability.
| Original language | English |
|---|---|
| Pages (from-to) | 2704-2711 |
| Number of pages | 8 |
| Journal | Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) |
| Volume | 42 |
| Issue number | 9 |
| Publication status | Published - Sep 2011 |
Keywords
- Neural networks method
- Optimization algorithm
- Wavelet analysis method
- Wind speed forecast
Cite this
}
Short-term forecasting optimization algorithm for unsteady wind speed signal based on wavelet analysis method and neutral networks method. / Liu, Hui; Tian, Hong Qi; Chen, Chao; Li, Yan Fei.
In: Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology), Vol. 42, No. 9, 09.2011, p. 2704-2711.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Short-term forecasting optimization algorithm for unsteady wind speed signal based on wavelet analysis method and neutral networks method
AU - Liu, Hui
AU - Tian, Hong Qi
AU - Chen, Chao
AU - Li, Yan Fei
PY - 2011/9
Y1 - 2011/9
N2 - To promote the forecasting performance of traditional neural networks for non-stationary wind speed signal, an optimization algorithm was proposed based on wavelet analysis method and neural networks method. This optimization algorithm employed wavelet analysis method to make signal decomposition and reconstruction calculations for original wind speed series attain more steady sub-series. Then BP neural networks method was used to build unsteady prediction models for each layer to realize multi-step rolling forecast calculation. Simulation results show that the optimization algorithm can attain high-precision multi-step ahead forecast results, respectively improve forecast precision of traditional BP neural networks method by 55.56%, 32.43% and 34.58%, and the mean relative error of one-step, three-step and five-step ahead forecast are 0.48%, 1.50% and 2.97%. The optimization has signal decomposition and self-learning ability.
AB - To promote the forecasting performance of traditional neural networks for non-stationary wind speed signal, an optimization algorithm was proposed based on wavelet analysis method and neural networks method. This optimization algorithm employed wavelet analysis method to make signal decomposition and reconstruction calculations for original wind speed series attain more steady sub-series. Then BP neural networks method was used to build unsteady prediction models for each layer to realize multi-step rolling forecast calculation. Simulation results show that the optimization algorithm can attain high-precision multi-step ahead forecast results, respectively improve forecast precision of traditional BP neural networks method by 55.56%, 32.43% and 34.58%, and the mean relative error of one-step, three-step and five-step ahead forecast are 0.48%, 1.50% and 2.97%. The optimization has signal decomposition and self-learning ability.
KW - Neural networks method
KW - Optimization algorithm
KW - Wavelet analysis method
KW - Wind speed forecast
UR - http://www.scopus.com/inward/record.url?scp=80355125428&partnerID=8YFLogxK
M3 - Article
VL - 42
SP - 2704
EP - 2711
JO - Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology)
JF - Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology)
SN - 1672-7207
IS - 9
ER -